{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/analogdevicesinc/pyadi-iio/blob/master/examples/cn0549/ml_fan_example.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import metrics, losses, optimizers\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import (\n",
    "    Dense,\n",
    "    Input,\n",
    "    Dropout,\n",
    "    Flatten\n",
    ")\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras import utils\n",
    "\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CN0540+CN0532: Multi-Label Classification Example\n",
    "\n",
    "This example demonstrates a simple multi-lablel classification problem where we wish to identify an event with an ADXL1002 (CN0532) accelerometer plugged into the CN0540 platform.\n",
    "\n",
    "The objective here is quite simple, the CN0532 has been mounted to a standard floor standing tower fan and the speed setting of the fan wants to be determined. The problem might seem slightly contrived but similar scenarios in the field can exist when performing anomaly detection, and it is desirable to understand modes of operation.\n",
    "\n",
    "Below is a picture of the Honeywell fan used for this experiment, and the CN0532 can be seem mounted in the second picture. Ideally the sensor would be mounted with bolts for maximal energy transfer to the sensor but painters tape is reasonable for the demo here, and does not require the fan to be taken apart.\n",
    "\n",
    "<img src=\"img/fan2.jpg\" width=\"250\"> <img src=\"img/fan1.jpg\" width=\"250\">\n",
    "\n",
    "The central button on the control panel sets the speed of the internal fans and this is what needs to be identified by measuring the vibration of the unit in each mode.\n",
    "\n",
    "\n",
    "## Data Collection and Import\n",
    "\n",
    "The first step in any machine learning application is to collect the necessary data to train and test our system from. This was done by using the script [collect_data.py](https://github.com/analogdevicesinc/pyadi-iio/blob/master/examples/cn0549/collect_data.py) co-located with this example. For convenience, this data is provided with the example in three files representing different modes.\n",
    "- [Sleep: mode2.csv](https://github.com/analogdevicesinc/pyadi-iio/blob/master/examples/cn0549/mode2.csv)\n",
    "- [General: mode3.csv](https://github.com/analogdevicesinc/pyadi-iio/blob/master/examples/cn0549/mode3.csv)\n",
    "- [Allergen: mode4.csv](https://github.com/analogdevicesinc/pyadi-iio/blob/master/examples/cn0549/mode4.csv)\n",
    "\n",
    "Let us first load these in and examine the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4096)\n"
     ]
    }
   ],
   "source": [
    "def readfile(filename, label):\n",
    "    if not os.path.isfile(filename):\n",
    "        url = f'https://github.com/analogdevicesinc/pyadi-iio/raw/master/examples/cn0549/{filename}'\n",
    "        r = requests.get(url, allow_redirects=True)\n",
    "        open(filename, 'wb').write(r.content)\n",
    "    data = np.loadtxt(filename, delimiter=\"\\t\")\n",
    "    x = data\n",
    "    y = label * np.ones(data.shape[0])\n",
    "    return x, y.astype(int)\n",
    "\n",
    "labels = ['Sleep', 'General', 'Allergen']\n",
    "mode2_x, mode2_y = readfile(\"mode2.csv\", 0)\n",
    "mode3_x, mode3_y = readfile(\"mode3.csv\", 1)\n",
    "mode4_x, mode4_y = readfile(\"mode4.csv\", 2)\n",
    "modes_x = [mode2_x, mode3_x, mode4_x]\n",
    "\n",
    "# Merge sample sets\n",
    "x = np.concatenate((mode2_x, mode3_x, mode4_x), axis=0)\n",
    "y = np.concatenate((mode2_y, mode3_y, mode4_y), axis=0)\n",
    "\n",
    "print(mode2_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each x output is a matrix of 100x4096, which represent 100 individual captures of length 4096. Each y output is the expected label or classification for these data vectors, which are of length 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4096)\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "print(mode2_x.shape)\n",
    "print(mode2_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data we can notice oscillations or frequency component differences between the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7fX7eTro1jqZDXJTdNUDrtKXnFlArIoSHLUY2mecLeGy2ZsIzKQdTR/FAWg7NY6ozd8sx1h6wHqH9sN757/rQD1vo37w2E3o1ZvEOxy7XNcKMOziA2cRnwplC8jXujiy+AO6XUq4CEEIMQFMenXwlmD+QUlo1QiYzkyNsPVccYWuasMWRl4+r6/d6dRnvTuhC2rl87hrYzM6bxBQ4Vi2k1H456M3lVnX+STU2YRix/VgW7RrUcLu+LzArCwulZTLXHM/K41KD+ITpyzV//nP5Rdz+5Qa+vL2XV2VyN0ZDUTb+t6S0M2E54ikpwa6jden7iVajAUuW7TnFg9/bKyPL9/PnTan8x2LuZ9fxbJrHVLfzegPXnZzfth3nt23HmdCrMQEODP7n8grtvMLKi6/8QdxVFudMikITRiYKIS46U9TWo5lmG6k7fLG6/Hbz8mKy/d41sBkv/a71DOckpRISVPp0OnNJvHNmksNjtsxIPGQ3V3DNR2t4/ZpO1LMY4fiSZ+dqw/JiC0XqyFxjxPK9aXygKw9F1abPa8ZR9I6Ut5GisOXF36xHSkUlJXYxDp4Sn7CAm3o3NjzWccqScl27InF3gnuDEOITIcQQIcRgIcSHwAohRDfLRYqqOsEG7ptVhc9XHbTa97bpwxFJhzOY/Ot21joJqPIFi3edJD5hQZlyWb252DcmKEXVxHLOINvGzFNYLDmX77myGG8Tm1WByWENY2G8gXDHh10IsdzJYSmlHOY9kcpPjx49ZFKS+z1mE3tOZltNYCvc5/0buvKQgb1ZoajsBAYIh16BUeHBtK4XaTgXVZlxZIpzhRBik8VCdVa4ZYaSUg4t052rGEGODIsKl/zfEtVbV1RNnK0JkXWhsMopCl/hVusohKgrhJghhFio77cTQtzpW9EqHpP3kcJzUhx4OikUioqlWZ0In1zX3a70V2hpwRvo+/uAR30hkD8xSjmhUCgUVYnXrurok+u62zrWkVLOAUoApJRFgO/THFYwQd7Kz61QXOSoV6Xy8fL49giBXbCut3BXWeQKIWqjh6GZFifyiUR+pORflE9f4ZqBLeuYt/u3MM535YryNqpNalezcoOuaF4aX5rYqk29SPN2r6a1vHJ9y+9YoTFpULMynXdL33gOvTaOsGDv54UC95XF42gr2TUXQqwGvgYe8olEfqSOQaIxX9A8xjc2xYuFiorZcEVviwbxu7v6mLc9SeTYTE8E54i1T9s7El7RpYF5++8nhzKqXV2n19j2/Ci7slevdGyKqOkgJYoRloGhX9/Ziwg9wFMg2KlHw3vKjRYxB7YRygr47+jWrE6oVA6mgJvKQkq5GRgM9APuAdpLKe2zZRkghAgUQmwRQvyu7zcVQqwXQuwXQswWQoTo5aH6frJ+PN7iGk/r5XuFEGV7Qt3AkziLxKeGcv+Q5lzVLc7j+7izLsO8B/oT7cFLXdF8cZuhd51Lkl8Z6/JFiImsGKXtivRczb++Se1qVuXNHCj7Gw0Cr1wNLEyZfQFzQ2w7kjBlEJ3gIJNpkIFjRu3qIQ7v6ckAutgisVlYcCALHxkEwJXd4oiwyFp7VVfj92DhIwOtvq97BjdzqsgU2typUYdkXKf6dmVdGtVk+RND+O3BAT6Xy11vqGpAAvColHIHEC+EuNTNezwC7LbYfx14W0rZEsgATF5VdwIZUsoWwNt6PYQQ7YAJQHtgDPChEMI34ywDLHt5lsREhvLfMW1467oudGponIumrCSMbUPnRjXZYtBjrCz0alo2s0xQYIDDBnRI6xhAG3k9MLR5GSVzn0a1nI8QTAn4bHNavTuhq+EkoilmqWF0OPG1q/H7QwPwZOFBk/kg0IHtqnuTaMNyI2VRx4my8MTcajmyCAoQNK5djd0vjeG6HtaK65a+TQzPb1u/Bk+PbWvef2xEKwBqhGmK5oZexgqwKlI/yrcj4iu6xJH8yljz/uqEYfxyXz+a1omgo5fbICPc7Up/CRQAffX9VGCqq5OEEA2BccDn+r4AhgE/6VVmAlfo2+P1ffTjw/X644FZUsp8KeUhtDW6vZvcxwnvTOhqGOASbBGT8cjwlh5d05Rz3xHjOtr3IJzx7Li2rivZMKBF+WzFYQ7s6L/c38+uUVv06ECrfVMDWrdGKIdeu8ROput6NCI20nsv3rL/DDYsH2qTmdeWGuFag/bk6NZW5bUiQrihV2Om2SgMU5xO9dAgVjw5lA5xUQgL1dgzPtrKtGWLKVW17VoEEuPG/e6BTfn9oQFWz6KJ7k2s79MxLopb+mgNehcPJkBDLX5n0+cLD7Hvq3W0SN5Xt4b1yNAy8NekEE2jx2ohzkO9esYbK0hvcalBb72sfHdXbzvT3JUORlxlITBA62z99uAA3p3Qhbia4QRUoKeBu7mhmksprxdC3AAgpbwg3Fus+R3gv2jZagFqA5m6NxVoSsf0bcYBR/XrFwkhsvT6cYDlWoSW55gRQkwCJgE0bmych8WbWP5IRo1OzWrBhqtixdUMZ+njg2j3vON8RkY9RVsaRoebU0PfNbAZUxfstjoeEhjgME/+w8Na8NjIVuQWFNPhhcXERoaSMLYNoUGBPPD9Zro1rknNaiGs3JfmcB1tR27G3RpH23mVtalnnXzQ1ED0b1HHas3vhtHhZsXszYWNjBpTgMnj2vL12sOGxwBi9DksU8P27Li2hFpMHpoaPlMK9+t7NuKbdYetOg+mj/f29Z0Z0bYuwYEBZF0o5Ej6efafyjFf95/ULC7pWJ93/tzHPYOb8dWaFF6/2n70svbpYSzecZJFO0/y1Jg2brt7R0eE8PIVHbi6e0NaxFY3ZyF2RKu61bmme0Nu7RvPZ6sOcio736m3YFBgAJd1bsA13RsyuFUMHV5Y7HSdi2/u7E3i/jNWKe+NmHNPX5o+/YfzD2dDi9jqbq1zP+eevjSLifAobbwzmsVUt1um9fGRrVwuefrpLd3p0rgmszcc5ZoeDR3WM63l0bFhVIWMJGxxV1kUCCHCKfWGag44TZWom6lOSyk3CSGGmIoNqkoXx5ydU1og5afAp6Cl+3AmmzNu6NWYuJphLN+bxmSLHvtVXeP4Rf/R97w8xuocI+3uKCrUnYkro0jybc+PoqikxLxc5bwH+lstXXl1t4b8vLl08STb3uiGZ4Zz8EwuHeKiqK7bmquHBrFx8gjCggOIDAtmub5oUvWwYL64rSd/70vj+/WHzUuF2nLP4GaMalePqz+yTr445fL2TJm/k/dv7Ep0Nc0c8vN9fc3plaPCg1n+xBAa1LQePVhaR+LrRJAybRwns/IcJoxzl6BAwbSrOlqtcjZpUDOXq4ndN6Q5zWIiuKab9gLfNdDYS2VI61jzOgyO0iy0rluDSD0VdVhwIHVrhNEzvpbddcd0qGd3nUeGt+Tw2fOMal+PqPBgbuvflNv6W68jsuq/Q8k8X8hl0xPpFW8/ejH1P9wdVTwxqjWj2muy/HxfP5JSMgyf8/dv6Ep87Qjztomljw8ypxU36lc2qBnOdT0bmdcWH9exPsPbxvL4nG1W9Rz1Scd1qs+eE9kcSMtlw+ThSAmrk89QUFRC8ukch8rix3v7mpdS7dW0FqfcXC7XRL/mtenVtBaPjmhllbBwWJtYXV7r+rE17Off+jSrRc3wEPNiSqbv+SEXFgpH5smKwl0z1AvAIqCREOI7YBnaiMEZ/YHLhRApwCw089M7QE0hhElJNQRMK4GkAo0A9ONRQLplucE5Xue1qzry4LCW/HxfP6slQ6de2YFqIYEseHiAoWval7f3tNp3lkIA4MCrl7DluZHm/f+zWE7VqAcXVS3YallI2yUi7xzQ1Mq89cqVHZl8SamyKyqR9GlW26woTMREhpobMdu7Dm4VY2Vv/vX+flbHnx7b1mxyiq4WzDd3atbBtvVrMPuevsRGhpmdBro3qWW1IFPTOhF2jbWzryw2MtTQ68cdggIE11rY2P+ZMoqEMW1cnhcWHMj1PRs7HOq3ra+NmEwNhRH/u7YzQ1vHmBfVKQtNakfw8339nJovG9WqRseGUXx8czc+uaW71bGa1YJ5cJhxQ3RH/6ZW8r94eXtSpo0zN2AADaOrcYUDc8plnRsY9nLrR4XTW19e11kTN6iVNk91W/94xnSoR9M6EVzTXVPOfz85xOF5/3dtZ+Y9OIB1Tw8nNjKMujXCuKpbQyb0akwPG9PVhsnDzds9bRSpo7UkHLlKP3dpOx7V510sCTfNN1loix/v7UtoUKBdByIyLJiPb+nOj/f25fu7ezv8jIP178bEMTcXmfIV7uaGWiqE2Az0QfvtH5FSOvV5k1I+DTwNoI8snpBS3iSE+BG4Bk2BTATm6afM1/fX6sf/klJKIcR84HshxFtoEeQtgfIvaeYh1UKC2PXSGIfHh7aOZc/LY2jz3CIAPrq5O5+tPEhicunX1LVxaa8uMEAQHRFC3RqhnMrOt/JbN1o60hXtGtRg2wujzL2dujXCuK5HI175QzNPeeKOamljNo0MwGA4p/P7QwNoFF3N4Sp1rtjwzHD+t2QvI9rZN7p1qofQIa4GT4xq7fH1TQniQoMDCQzQFoF6ZHhLp4vN2J7vjNb1Itnx4mg7BWxJh7gor6+f4YwxHUpt8D2aRNOtSTTPXGI/pzWuY30WbD/Bjb0bUSLhL31BpYn94r0u08BWdbiiSwOesJn7AW2kY9mYLn9iCKApWRMRIYHmpX1BU86mDpvRdz+mQ302TB5OZGgwgQHCaZxKeEgg/x3TmpNZeSzeedK8tsQTo1qzOrl0xDyyXV2rFS1NVAsJ5M4BTbldH+lZPjO2igng5j6NeUhX3EbHLZkxsQfZeUV0e3kpoCltf+JUWRikHzcZ9xoLIRrrLrWe8hQwSwgxFdgCzNDLZwDfCCGS0UYUEwCklDuFEHOAXUAR8ICUslJGj1uOOAa3imFwqxhz4/3HwwMNXS5NPRHL3mtYSOnDvSZhmOHchyNM9lpTj79RrXCOpl9wayKsnd5Tvql3qWdLVLVgesZH06pupJXisMTRymTuElsjjDeu6Wx4LCgwgN8fGmh4zBUvjW/P8/N2mhuUjZNHOKz75+ODOHTmPHd/XZqt2J2IfmeKwt/8dF8/h8em39iVF861M6+0uDphGLUc/L7lJTQokHcmGC+X6g62pqgPbnS9KoKtg8TP9/U1l82Y2IOmFvmT7h/SAoCXxndg85EMalULsZtvCTewJiQ+NZSIkCCiI0q/N0dms7/+M5izuQUuFYQlQYEB1IoIMc+JdYjz78Jjrp70/8N63sC2c+lW5IiUcgWwQt8+iIE3k5QyD7jWwfmvAK+4cy9/s/6Z4RRYrN1dPyqME1l5DleYM9n3Y6qHcvDVS8gvKrEyzzSoGW412fvuhC601iNpezSJtnuoFz86iMLiErPiWvrYYKdr/FoSWyPM0Ob+472OG53KSFzNcI5lXuCm3k2sFJ8zWsRG0iI2kvkP9ifzfCE/bUp1+OJfDAghrJbk9STQsKKx1dlG3liusPQOG97WcZCjyfQspeS5S9shwLyomC2Oevp3D2zKaAszHmiT381iDKu7pEVsdTYfyfRrJD+4VhZPAUellCcAhBATgauBFGCKTyWrotS1MffMf3AAR9IdB+GNal/Pyj7s6kUY36XUdmzUcwwMEAQGWHvs+Cr8359sfX4kJ7PzzOuP7Hl5DPlFJZzNyadGeDDHy7gATKeGmqlwUKsyvtkKrzNrUl9+3pxqt0qjLxFCcOeApub05Kb5KXeYPK6dV2WZMbEnO45nuXQz9jWu7v4xMAJACDEIeA0tzUcXNM+ja3wq3UVATGRopYlI9ga/3t+P9NwCf4tBTRuTiUkpmiaBKyp1i8L3tGtQg3YN2vHEKPs5D1/Tq2kt5j7Qn07lNLWWh+iIEAa29H/nxZWyCJRSmlb+uB74VEr5M/CzEMJ+BXPFRU/Xxr4NknLFnHv6Uku3EXtjGdxf7u9X5lGIomIpi/nJG3gSxHgx41JZCCGC9CC64ehBb26eq1B4HUuvMXeCF13RrXG0lYu0QqEwxlWD/wPwtxDiDHABWAUghGjBRZiiXFG1cBSZrVAovI9TZSGlfEUIsQyoDyyRpQ74AVyEKcoVVYuAAMGAFnW4ySDbq0Kh8BXNaHsAACAASURBVC4uTUlSynUGZft8I45C4Rnf3uU4AlahUHgPNY5XKBQKhUuUslAoFAqFS5SyUCgUCoVLlLJQKBQKhUuUslAoFAqFS5SyUCgUCoVLlLJQKBQKhUuUslAoFAqFS3ymLIQQYUKIDUKIbUKInUKIF/XypkKI9UKI/UKI2UKIEL08VN9P1o/HW1zrab18rxBitK9kVigUCoUxvhxZ5APDpJSd0VKajxFC9AFeB96WUrYEMoA79fp3AhlSyhbA23o9hBDt0FbNaw+MAT4UQlx8CzQoFApFJcZnykJq5Oi7wfqfRFtd7ye9fCZwhb49Xt9HPz5caEuVjQdmSSnzpZSHgGQMVtpTKBQKhe/w6ZyFECJQX/fiNLAUOABk6inPAVIB09JvccBRAP14FlDbstzgHMt7TRJCJAkhktLS0nzxcRQKheJfi0+VhZSyWErZBWiINhpoa1RN/2+0OIF0Um57r0+llD2klD1iYvy/qpRCoVBcTFSIN5SUMhNYAfQBagohTNluGwLH9e1UoBGAfjwKSLcsNzhHoVAoFBWAL72hYoQQNfXtcLS1vHcDyyldu3siME/fnq/vox//S18/Yz4wQfeWagq0BDb4Sm6FQqFQ2CNK1zPy8oWF6IQ2YR2IppTmSClfEkI0A2YBtYAtwM1SynwhRBjwDdAVbUQxQUp5UL/WZOAOoAh4VEq50MW904DD5RC/DnCmHOf7CiWXZyi5PEPJ5RkXo1xNpJSGdnyfKYuqjBAiSUrZw99y2KLk8gwll2couTzj3yaXiuBWKBQKhUuUslAoFAqFS5SyMOZTfwvgACWXZyi5PEPJ5Rn/KrnUnIVCoVAoXKJGFgqFQqFwiVIWCoVCoXCJUhYKhUKhcIlSFgqFQqFwiVIWCoVCoXCJUhYKhUKhcIlSFgqFQqFwSZDrKlWPOnXqyPj4eH+LoVAoFFWKTZs2nXGUSPCiVBbx8fEkJSX5WwyFQqGoUgghHGbrVmYohUKhULhEKQuFQqFQuEQpC4VCoVC4RCkLhUKhULhEKQuFQqFQuEQpC4VCoVC4xKfKQgiRIoTYLoTYKoRI0stqCSGWCiH26/+j9XIhhHhPCJEshPhHCNHN4joT9fr7hRATfSmzQqFQKOypiJHFUCllF4sFxBOAZVLKlsAyfR9gLNBS/5sEfASacgFeAHoDvYAXTApGoVAoFBWDP8xQ44GZ+vZM4AqL8q+lxjqgphCiPjAaWCqlTJdSZgBLgTEVLbRCoVD8m/G1spDAEiHEJiHEJL2srpTyBID+P1YvjwOOWpybqpc5KrdCCDFJCJEkhEhKS0vz8sdQKBSK8lNcIikuqZpLWftaWfSXUnZDMzE9IIQY5KSuMCiTTsqtC6T8VErZQ0rZIybGMLWJQlEm9p48R+b5An+LUSn5c9cpNhxK97cYlZapv+/iyg9Xm/c7TVnMoDeW+1GisuNTZSGlPK7/Pw38ijbncEo3L6H/P61XTwUaWZzeEDjupFyhqBBGv7OSLi8tBeBcXiHxCQv4fNVBP0tVObjr6ySu+2Stv8WotHyeeIgtRzLN+7kFxRzLvOBHicqOz5SFECJCCBFp2gZGATuA+YDJo2kiME/fng/cqntF9QGydDPVYmCUECJan9gepZcpFBXO2RxthPHNOof51hQKO+74aiOXvr/K32KUC19mna0L/CqEMN3neynlIiHERmCOEOJO4AhwrV7/D+ASIBk4D9wOIKVMF0K8DGzU670kpVTjXoVfCAzQrKJFxVXT7uxNpFTfgbv8tee060qVHKfKQgjxPgbzAyaklA87OXYQ6GxQfhYYblAugQccXOsL4Atnsipgx7EsLn0/kT8fH0SL2Eh/i3NRMnfLMQCzKWHJzpMczbjAnQOa+lMsv5B5vtDfIlQYeYXFbDmSSd/mtf0tit9wZYZKAjY5+VNUIn7bpk3ljHhrJW8v3ednaS5O1h06a7U/6ZtNvPz7rnJdc+W+tCo5SVz8LxhZbD2ayYWCYqbM38kNn60j+fQ5f4vkN5yOLKSUM50dV1QuLF/dd5ft57GRrZzWn7f1GP2a1yEmMtS3gvmZCwXFfLMuhW2pWbx+dSf2nsyme5Nabp1bYuPmuDr5rGG9P7afoE29SJrFVPdYvlu/2ABAyrRxAGxMSSe6WggtYj2/VkUyc01Kua/x1epDrDuYzse3dC+/QF7mbE4+V3ywmrEd6nH6XD4AGT4aTWWeL6CgqITYGmE+ub43cGvOQggRAzwFtAPMn0ZKOcxHcinKgG3DZiI+YQE39GrMa1d1NJdl5BbwyKytdIyL4reHBlSUiH5h8tzt/LJZMx8dTT/PP6lZrH9mOHXdeDGLLL5TW/fZ7LzShuP+7zYDsPX5kew7lUOvpu4pIyOu/VjzLjIpj8pChxcWM7RNLO/f0BWAI+nny33NKb9po7Klu04xtHUMQYGVJ12daeS04VA6zXXF7a0YiZISSVGJJCQogE9XHuDVP/YAle83t8TdX+Y7YDfQFHgRSKF0wlkBnMrOo6CoxLy/63i2Sxe5w2dzySssLtd931q6jyd+3AYYTy6dzs4D4IcNR7Q6UiKlNDeCqRnlf+ErO/tP5Zi3/0nNAuBcXpFb55ZYmFqu+djaRfThH7bY1b/p8/Vc98lah4rb6b0qabBWYXEJR86eJye/yGzqBGtFCpCTX2R+3jzl7q+TeG7eDo6Xw630ZFbpO/j3vjSaP/MHWRdKFfqaA2coLC5xdLodAUJ3ZiiR6H4Ndr9R1oVCssow2mj2zB+0enYhP21KNSsK8J4y8gXuKovaUsoZQKGU8m8p5R1AHx/KVaUoKi6h96vLeHzOVnPZJe+tov+0vxyeU1IiGfzmCnOPtKy8t2w/P21K5bv1h8kvslY8GbkF9Hp1mXk/PmEBTZ/+gynzd5bW+RdMUgYYhHUWlThuNH5MOkp8wgIW7zzJtxYussmnc6zqrdhrnylg5/FsoGz2/EInMvmTqb/vYtCb9oFkdSOtR2bj3ltl9bx5yg8bjtJv2l88+eM24hMWUORBw55XWEyf15aR8PM/fL7qIFN/30VxiWTHMa1zsPVoJjd+tp43F+91+5qmn7BEOo667vziEjq/tMTta9pi6uiZOJuTX6brlJRIXlmwy6cxHO66zppalBNCiHFoQXENfSNS1eFo+nkaRofzyUotQGvxzpNun2tqGFbu805qksm/7rAr6/ryUsO6M9cepmPDml65b1UgwEBbfLj8AO/p5hRbnvzpHwDu+absPhzFJZLgQM/OyS8qobASuuSu2n/Gan/lvjSCAgVpNg3b4bPeGaX+uCkV0Hr0QW5+h68v0nrnv2w5xi+6xxqUjpwzcjUT4t6T7k9Qm1yDpYSNKRlun1ceCmwU5OKdJ2kYHU77BlEOzzmVnce6g2f5bJUWAPjTff18Ipu7ymKqECIK+A/wPlADeMwnElUR1h08y4RP1/Hg0BZMX54M4NGLbhouC6NkJhWAbY/Gluy8Qi4UFLtl1/c2Ukq+W3+Eq7s1JDzEwxYX+GvPKcKCA2kUXY2QoACrCFoTh8/mEp+wgLev78y6A+nMTjrK/lfGEuwlm3mJGyOLuVuOcb6gdDR418wkQ6+o7LxCaoQFe0WusmD7SUwT8r6mzXOLeGxEKx4Z0dJl3S9XpxiWT/19N9f3bMx6/Xt19bss3H6CD1YkM/+BARTqownLcywHGL6IM/lu/RGeGtPGvG/qsDiby+htMZrzxMzmKS7fDCFEINBSSpklpdwhpRwqpewupZzvM6mqAAfSNJOESVGYWH/Q2FvGkkU7TnDz5+sBTcGcyyvkwxXJlcpm3WnKEnq/uowLBeWbUykLy/ee5tm5O5i2cHeZzr/jqyRu/Gw9A99YzjdrjSOtt+lzF4/N3sbsJC1P5WerDhKfsKBsQttgMlucys7jyR+3Wc1NSSnJKyzm0dlbeebX7eZyW0URn7CAqb/votOUJSzaccIrcrlDflEx360/bH4e3WmArrOYz3H0HOfkF3k8J/H2n9Yu4DPXpJDw8z9un9+vhRYX8fHfBwBtlGTUyOcXFbPreDb3fbeZHceyKSwp4TPdYmCp0AuKi5mjPy+2o4Clu06x+Uj5RiD7T+Ww+UhGmdsCX5qVXY4spJTFQojLgbd9JkUV40TWBUOzD8D1n67jtwdLvYvWHzxLy7qRrNqfxvgucRSXSO791nqe4pUFu5m18SgtYqozqn09w+uezs4jMEBQu3qpm+u3Xko5se7gWRrVqkZczXC7YxcKi5327k9l57Fox0km9ov3iiwAufnay3kmp9T76M9dp+jUKIrYSM9GOp68vH8bzEGUFdP0w9QFu/lt23EGtKzD+C5asuTXF+01N16u+DzxEKC57I7pUN+tc/KLigl1135jwLt/7ufDFQfYdjSTszkFpGa4buA3pJQqui1HM4gMC6ZVXevA0PHTEzmQlkvKtHHM33ac2DK4bL+gz7ct3HGSW/o04YnRrZ3WX7zzFM/O3W5V9svmY1zdvaFm5/9jNxP7xvP+X/vN5i/QTE+HzuTaXe+ebzZRWCxpV78Gz8+zbgPu/joJgHo1wggPCaRPM88D+P7cfYo/d5+iRlgQfz851Gnd4hJp18E4kn6eFXtPM6R1rIOzyo67Zqg1QojpwGzA/A1KKcs3O1vF2Ho0kys+WO2y3mXTE83b13+6zrxdOyKUsGD7wZzJY8PIjHX6XB6Pz95GYrJmNzYNR5NS0nl2rrHC8pQJuoxGQ11XQ+17vtnE1qOZDGsTS6Na1bwij8kLReoGkKLiEu76OolmMRH89Z8hHl1rzQHXIz0T670YGFdUUkLy6Ryzd5BlehB3FYUlqRnnST59zmFkfm5+Efd+u4kru8bx+JxtfH9Xb/q1qFMm2Xfok/RzklJd1DTm2bk72X0im69u70nzmOrm5+JAWmnja+RJ5ojdJ7JpFhNhpQCzLhQyfXmy3cjeiG/XHbHa/8+P24gKDyY14zwzEg8xI/EQ8bWtn92iEmnoBGF6R6/9eC0XLEaL91rMb53Uf3MjZeMu2XlFDuccQYvFufZj4wSOX65O8auyMM2YvGRRJoF/TZxFasZ5txSFM26esd6w3GQTNRruz1h1yKwoTOw8nmXnxukrjFRFflExRcWSiNAgc+yBN22lwuymqP/XhUixefmu+2Qt/ZrX5tERrRj/wWou6VCPewY395oc5eGNRXvN5i2AMzn5fL02hVv6NCnT9ZbvTWP53jQ7hX40/TxpOfkcy7jAqv1nzJPRicln6Nm0FgLMsQupGefZfzqHRtHVqB4aRL0o41FaYDnn0Xaf0JTNbV9q3vW7Xxpj2Elyl7HvruLyzg0cOiSUhbv0UYAJW++1HceynKYzuWDj8r7IA+eW8lJYXOJQUYCx9583cEtZSCmdj4cuIv7ac4p29aOsXqSCohIGvO67HPSLd54C4NHZW3l09larBsHWZc9bNnUjnv5lO69d1dHKvr7mwFku79zAqt5VH65h5/Fsfn9oAMI8CvCczUcyiKkeSnZeodnbI+tCodmd2DSyMClTWzPuhkPpbDiUziPDW7LtaCbbjmZy2AuBYq745s5e3DLD+SSvpaIAeG2h5q3Tw83IcXcZqK+NMP1G+4a0/fOLiYkMZXWC1qcb+84qzuWXxpdYPmdncvK5/7vNfH9Xb3OyRG/R9vlFjOtYakIryzM8f9tx5m/z3coEtoOICRYWgcqGqySWvvKoc0vdCyHqCiFmCCEW6vvt9KyxFx13fJVEn9dKvQsyzxfQ6tmFFSpDem6prf4f3U+8IjAF7v28udT8YOQObIoluPT9RItRgKSkRDoNMswrLGbrUc0zqai4hKs+XMPAN5Yz7r1Ss90Oi89rUqKuApUsj3+//oiTmt6hVd1Iru1eNs9x2x5pWVlz4Ay5Fg2/UQNSUFxi5XdvqShs6TH1TzYcSueNxXvNc0beZMF2307Qh5TTi60yB8PZctRFIK2tJcJbuGuG+gr4Epis7+9Dm7+Y4QOZ/IZRQIspJ0xF0m/aMvIKS+jauKah26cvGfvuKrMZAWDBPyf44EZYvuc036w7bJdq+aBuh565NoXqocF8/PcB9rw8hjCDIIMnf/qH37YdZ8bEHvS3safHJyxgxRNDCA2yfulPZuUREVp6rTcX7+GHDUdpHhNhLjMpr4oiIjSI16/uRHBQgMfK6eqP1pTr3kfTz3Ms8wI3frbeaoL40dlbrerZNn7vL9vv1vXPnMtnrRsefZUNW88kTzlZxsjziuRMTj6D3lhu5Z1VkbirLOpIKecIIZ4GkFIWCSH8I7EPSbUxYWxPzeL7Db7vqdqSV6g9+BWtKAArRWHCHbPB/lM55gjnnPwiQ2WxRfdMunNmEq9c2cHu+Lj3VjHjtp5WZem5BYQHl3ppfbD8gLnchGVKB18zrE0s1UO11+ahYS0qZCRjyUCLJTmddWRMgaKgmXD+z80sxJYBbQrv8/jIVrxVhozQ6w+eZfuxLL8pCnA/3UeuEKI2umnatJKdOycKIQKFEFuEEL/r+02FEOuFEPuFELOFECF6eai+n6wfj7e4xtN6+V4hxGgPPp9HWPbFjmde4LLpiWbTTFXl4eGuA5q8wfpD6ZzVG3BHgU+WxW8tsX9hcguK7WzFl7y3isPpzr1KKipIDODugc3M2+Gehmj7CUeeR6v2p5lTxHRq6DhCWOEdXr+6IwNbls1D7fpP1zF1QdnijryFu8ricbRlT5sLIVYDXwMPuXnuI2hJCE28DrwtpWwJZACmuY87gQwpZQu0mI7XQZsfASYA7YExwId6oKDXsWzM3E00V9m5sVdj9rw8pmJv6ob592xugetKOpdPL58XmjexjLj39kRwRXPLjA1M/nUHyafP2Zn/FN5nQMsYhx5oo9rV9dp9ruwa57VrWeLWE6LHUwxGc6G9B2gvpXQZRimEaAiMAz7X9wWau+1PepWZwBX69nh9H/34cL3+eGCWlDJfSnkIbdnVXu7I7SmWMQW+DJuvSAICICw4kLeu62zn1eQr9p7S8u+cys6juESSdaGQ+IQFVXahekss1UNkWDBvXN3Jb7J4g582pTLirZUVlvvo30xczXDqR4Vz/xB79+7R7et5zQrw8hX2Jl5v4El3ohfaMqndgBuEELe6cc47wH8BU8tbG8iUUpq67amASQ3GAUdBmxNBM3PVtiw3OMeMEGKSECJJCJGUlla2SFzLOcFL3090XNEBlbF3ZtJ/V3Vr6FU/dWfcMmMDxzIv0PvVZbR9bhGdXyx7Vs7KRu3qIVb71/Vs5CdJFFWV/1rkfjIhhLUnYFnora+hElGGfGru4K7r7DfA/4ABQE/9r4eLcy4FTkspLVN3Go3bpYtjzs4pLZDyUyllDyllj5iYGGeiOaRuDffSD/Q2WNgmZdo49k4d69XhZFnZ/8pY87YjU8n2KaN8KsOrun21vF4q3uK2fvG09MLKc2pt88rPnHv68p+RregV772YlobR9qlwPOGO/s7XaG8ZG8kxN9KqOOK/Y1rz+cQeLH50kDn2ydu42xXuAfSXUt4vpXxI/3vYxTn9gcuFECnALDTz0ztATSGEyQurIVq6c9BGDI0A9ONRQLplucE5XqVlXfcagh/u7sP8B/uz8JGBdsfevLazt8Vym8s6N+DL23taZU6tU91YAUb6OIupr/3qPWXK5e2ZNcl+CZbOjZynam9TL5I59/R1Wsfkwnrg1UvKLuC/jAYObPfeoFfTWjw0vKVXJ+272Dwnfz4+mNHt3esYPjK8JZPHtbUqs8wft3HyCDo2jHK6xoor7hnUnMiwYFrX811nxl1lsQMwznDnACnl01LKhlLKeLQJ6r+klDcBy4Fr9GoTgXn69nx9H/34X1KbRJgPTNC9pZoCLYGKc38xICBA0KlhTdrWr2F3LCq8tBH2Rk/WE96/oStDPcgJY3phB7Uq20isKnDw1Us4qDfitQ0U56y7+/DTvfbKoJ3+245uX49eTWvx1nWdubF3Y8N7LHxkIHMf6O/xhPeUy9qZt9+d0IX5D/b36PyKIvGpoYzvos133T+kOSPalj/v0Kqn7DMFDWnt3efQmx3s6Gql5scnR7emRWx1ggK05jMoQBAaFMCh14w7CzXCg+2ejY4WiixG72xYRl476uQZkTJtXIU4W7gdZwHsEkJsAMzO3VLKy8twz6eAWUKIqcAWSgP7ZgDfCCGS0UYUE/R77BRCzAF2AUXAA1JKvzkb39zHuMEwIrZGKPttVlfzFV/e3tN1JRu6x9fi+Lbj1K/Ei8R7StM6ETw1pg1Nalcj+0Kh3cJHE/s2oW39GiT8omUiDQ8JpIeBueKKrg3YdSKbOvocxVXdGnJVN+Oo7drVQ82K6J7Bzfjk74OG9SwZ1CqG2/o3Zdme06zaf8ackXb6jV0pLpE8MmuriytUHA2jq5lHTx3iosyBmOXBqHH76vZeJJ8+R1hwIF+vPUxsZGi53EVtG9xnx7Vl9sajHr+TT49tw019mlAsJd+vP2I2NU+5vD21q4fw3KXt7NZBCQwQ5sBId5txy5UBN04eTtOn//BITl/jrrKYUp6bSClXACv07YMYeDNJKfOAax2c/wrwSnlk8Ab7po4lyAMNbhplTLmsHS8v2F2ulAId4mqw45h1wNz7N3TloR+2EBES6NGIwsSb13TigaHNmbvFdzl3Kpqo8GDGdHA8CH5xvOYpkvDLdhrVcmyHvql3E2Ijw7jMQw+yuwe6pyy+vkN7BWbe3stqOdVLO2n3q0zKAuA/o1rTMLoaY9rX42xOvs8S55nmhJ65RDPbBAhBw+hw4qLDzWlhfrm/Hy1jq9NxyhI6N6rJj/f0JTBA0PwZ68b1zgFNzTm5AO4a2Iy7BjZzGGQ694H+hslCTckpX72yI69e2dFcHhMZykvj7T2POsZF8dtDA7j1iw2s3JdGqJMkipbNScIlbXn4hy20qlvdZ/MO5cFd19m/gRQgWN/eCFyU6ck7xNmblkyEBAUYLtHpiCdGtWZCz0Zc37MxrS3mQ5b9Z7DHcv16v72Jom9zLV++owfLlahhwYG0qVfDfJ2qTs1qwYYJ9YzY/NxIFj86yOHxwADBFV3jPB7em6K7nfHFbaW+IQEBwunaE5ufG+nR/b3NG9dorsFhwYFM7BdPQIDg5j5NWPv0MCYNamY+XhbcaQ/vGNCUUe3r0aZe6XvZrXE0kWHBpEwbx9z7+xESFGD4OwUFBjBGXx/GMndUrYgQu7qgzUs4W5HOHVY8MYQf9Lmx2/ppGYZ7Ophof3dCF5Y+XtoWxNXURvjuPEMAH93UzXDe1Fe46w11N1rswyd6URww11dC+ZPnL21vWP6Xgwb+94cGsCbB2v5qiuxtFlOdaVd3IjwkkB/v7cvgVjFsenYEzWO0uYyxTnrAtgQHBvCsxSRZu/o1zMNbo5fures6Wz2IJmqE2T+Ig1vFsPPF0Sx4eIBLrw0TDw1r4Va9imRNwjAaRru3pkatiBCqhTh+KY3SlbhDWHAgya+Mdaqoh7Vx32OuVkQI8x/szzvXd2H5E0N4YlSrMslVVq7rYe8aLISgflQ4z1zSlqu6xjHlsnYOEyt+d1dvt+5zQy/nLsiOvk/LjtLP9/Xlo5u6WR3v3iQagA8tyjc/N9LpqNLEZ7f28Lgxjq8TYW7sh7Wpy8FXL7FbBMrE+C5x5rYAStdxMc2PfHBjN167qqPhuQBjO9Y3nDf1Fe6aoR5AMx2tB5BS7hdCeH91jUqAo4eyukEjC5oN15akZ0fYZQGNCA1i5h2l1jdTD8ZoSNwitro5z5IlppXsXry8PRP7xXP6nJb8zKhn6si+/tN9/ThlkDQtIjSI9g2iaN8gii9WHzI815IJvRozb+txjlRASnB3KW/mUW8RFBhAi9jq7DtV+humTBvnUWru3x8aYF6hsFPDmnRqqHnjPDisJf8zSJXiilv7NuFrB0vMmhjSOoYmtaoxc+1hRrevy9UOniFLggIDuK1/U75ff4QfN6USGxnK05e04bHZ2hrv/VvUYe/UMaw7mM7GQ+lMGtzMvJ64KQYormY4r13lfITijlmmu0H69zsGNKV9XA36NXeeZuPrO+zjfEd6wQ3eE0tEl0Y1mXxJW67WFe+4Tlpa96d/2e7stArD3bcrX0ppzs+gu7ZWnZy+HuAwF7wHnzYiNIioap65pm55biQ39W7MvqljWeLAPDKmQz2+u6u3eQEd00vnSW+zVd1IBrZ0z+tkzj19+fm+voZeKtVDgljymLWc7ua9sfUSc9Q7+t+1nena2LlrqyXl8QhZ/sQQXrisHbMm9eHn+/q5PsGlLOVTXB3ioqx6nZZ44vNv+kpu6t3EaePXtn4NPrixG0X6vFrfZrUdLvFrRLE+7zKyXV2u7GqtZEKDAhncKoYnRrc2P7OAOZnkrw+4/317GscUGCAMFYVJUS16dCCbnxtZKTwChRDcPaiZQzOZv3F3ZPG3EOIZIFwIMRK4H/jNd2L5D0e+zjFlWC/YHZY+NogSCdERIbxypfGQ0+SRI4SwSu0dFhxYbhurM3rpwYdf3taT+duOmyddV/13qFkZ/nxfP3Pa7a/v6OWWB8fcB/rT/oXF5v03r+lEneqh5nVEfntwANERwTSMrsZlnevT+tlFLq9Zr0ZYuSYFm9aJoGkd90xw7vDxzd34dt1hDqTlGo7kfMWahGGs3JdGwi/baVMvkuISyf7TOQgBPeOjWbrrlOF5JnPLd3oW3dUHznKbmyZJ0NxDAfMa6X8+Pph9etoXR9zUuwk39XZ/5cBNz47wWnxQ3RphpGZcICo8uNI2zpUNd5VFAlqiv+3AJGCBlPJzn0nlR0zpwfu3qM1HN3fn183HuL5nI595JzgKBNz2wijO5uQTERpkNkf4CyEE47vE8cSP27i1b7zVWtsmm7CpniU1qwUbLk0ZERpE0zoR5jWKG0VXsxqJNalTzdwDdce0dN+Q5jxlkELBnzSpHcHkce1cJTH38QAAIABJREFUVywDveJrkZpxjB5Nokk6bJ3TqUHNcPNILShQmHOcCUpt4iZ+urcv13y8lusN5iWOemhevKxTAwqKSrhCT2LXIrY6LbwcZ2QUJ1NWPrmlO8v3nKZ+lP0obfqNXa3iKvxNz/joSpG7y6myEEKMBxpKKT8APtMnumOA7kKITCnlT87Or4qYIjUfHdGKGmHBTOwX7xc5osKDrQL8KpINk4cTbGBG2f+K+xHKTetEsPyJIXy04gCvLyp1X+ymm5V+e2gA5/OLiDWI8Qi0aNQsFdCVXeM4m1vAyn3Wub8KiypHSpGK4tWrOnLfkOY0qR1Bq2cXckf/pozrVI/VydqiRW3r12Bsh3o8NrIV936jZduxnbA3jUi3PT/KanGpNvUi2XPyHA1qepbeIiBAcK2B0qms1Kke6lBek/tyZaFRrWpWymLaVR3p4oF51lu4Gln8Fz04TicE6A5UR1s576JTFjGRoT417VQFTKYEd3lqTBu7vFpNamujj/uGNDcri6RnR5gDpaqHBjl0EXQ09/D29V04fS6PLUcyueeb0pRjbSrQI6QyEBYcaB6RmqKGhRDmCd6QoAA+urk7AJ/e2oOfNqXSMDrc0EXadm5t9qS+3Pj5Op6/1DejIoXndG1Uk182a4tSxdUMZ0Iv9wODvYkrZREipbTM+JoopUwH0oUQEY5OUvy7uM8g5bKR/32Ii6y8t/WL56s1KU4DH2MjwxhtMfH624MDnMbGXOy4Mo+2iK1OwljNRNe+QRRj2tdzmjAzqlowCx6uON99hWtu7tOEFrGR3PDZOr9mtnalLKItd6SUD1rs+t99QFFpMRqduJp/eP7SdiSMbUOQBy6w7RrUqJTRrpWVj2/p7m8RFB4ihKBPs1o8PLwlV/loYSN3cKUs1gsh7pZSfmZZKIS4Bz8n81NUPVwpi4AAQViA/WT+3qljyLeZl6gWEsj5guIqv1qdQuEOQggeH1mxAZm2uFIWjwFzhRA3UpreozsQSukKdwqFmcSnhtp5QH12aw++X3/YowAlS0KDAu0CDzdMHkGxo5gYhULhdYTlUqIOKwkxDG0NbICdUsq/fCpVOenRo4dMSkrytxgKhRUr9p4mMizIMNJYoagMCCE2SSkNF7ZzK85CVw6VWkEoFJWdIWXIDKxQVBYqRzIdhUKhUFRqlLJQKBQKhUt8piyEEGFCiA1CiG1CiJ1CiBf18qZCiPVCiP1CiNlCiBC9PFTfT9aPx1tc62m9fK8QYrSvZFYoFAqFMb4cWeQDw6SUnYEuwBghRB/gdeBtKWVLIAMt5xT6/wwpZQvgbb0eQoh2aFHk7YExwIdCCP8mS1IoFIp/GT5TFlLDlNA/WP+TwDBK04TMpNQFd7y+j358uNCircYDs6SU+VLKQ0AyBsuyKhQKhcJ3+HTOQggRKITYCpwGlgIHgEwpZZFeJRVt1T30/0cB9ONZQG3LcoNzLO81SQiRJIRISktLsz2sUCgUinLgU2UhpSyWUnYBGqKNBtoaVdP/G0VsSSfltvf6VErZQ0rZIyZGZSJRKBQKb1Ih3lBSykxgBdAHqKmvtAeaEjmub6cCjcC8El8UkG5ZbnCOQqFQKCoAX3pDxQghaurb4cAIYDewHLhGrzYRmKdvz9f30Y//JbXw8vnABN1bqinQEpWXSqFQKCoUt9J9lOnCQnRCm7AORFNKc6SULwkhmgGzgFrAFuBmKWW+ECIM+AboijaimCClPKhfazJwB1AEPCqlXOji3mmA89XpnVMHOFOO832FksszlFyeoeTyjItRriZSSkM7vs+URVVGCJHkKD+KP1FyeYaSyzOUXJ7xb5NLRXArFAqFwiVKWSgUCoXCJUpZGPOpvwVwgJLLM5RcnqHk8ox/lVxqzkKhUCgULlEjC4VCoVC4RCkLhUKhULhEKQuFQqFQuEQpC4VCoVC4RCkLhUKhULhEKQuFQqFQuEQpC4VCoVC4JMh1lapHnTp1ZHx8vL/FUCgUiirFpk2bzjhKJHhRKov4+HiSkpL8LYZCoVBUKYQQDrN1KzOUn8i6UEhxiYqer6qczMqjzXMLWXfwrL9FUSgqBKUs/EBufhGdX1zCa3/s9rco5WbNgTPEJyxgx7Esf4tSoaxOPkNeYQmzNx51XVmhuAhQysIPHEzLBeDzxEN+lsRzpJR8vuogWRcKAfhr92lAUxr/Js7laZ+/ROVWU/xLUMrCD0xfvt/fIpSZtQfOMnXBbp6ftwOAgAABQHGJP6WqeKb8tguAlDO5fpZEoagYlLLwA6aRBUBJJZ+3kFLy4YpkjmdeACC/SNMKJ7PyeObX7Xy68iCg9bSz9d72v4n2cVFsT81CZW9WXOz4VFkIIVKEENuFEFuFEEl6WS0hxFIhxH79f7ReLoQQ7wkhkoUQ/wghullcZ6Jef78QYqIvZa4I9p/OMW8XllTuLvmJrDzeWLSX27/caFW+/lA6368/Yt7/cMUBur60lO2pWXyzrjzLn1d+LB0Ttqdmcdn0RH7YoOYuFBc3FTGyGCql7GKxJmwCsExK2RJYpu8DjAVa6n+TgI9AUy7AC0BvoBfwgknBXAwUFFVeZSGlpN+0vwDYe+qcVigc1y8ukVw2PZHn5momqn2nznHT5+vIKyz2tagVgpSS1xftofkzf5jLtusT+3tPZvtLLIWiQvCHGWo8MFPfnglcYVH+tdRYB9QUQtQHRgNLpZTpUsoMYCkwpqKF9hWFxZXXfJF9ochqf+fxLLanuuf1dCAth0vfT2R18lmSUjJ8IV6Fs/N4Nh+tOGB4bObai3s0pVD4WllIYIkQYpMQYpJeVldKeQJA/x+rl8cBlmP5VL3MUXmVoLC4hL0nz3HoTC47j9s3tO/8ua9Szlvc+dVG+k5bZlU27r1E3lq6z63zh//f3+ZR08XiMRQU6GRYBcQnLGDT4QyklBSXyEo9alQoPMXXEdz9pZTHhRCxwFIhxB4ndY3eROmk3PpkTRlNAmjcuHFZZPUJry/cY+UimzJtnNXxr9ce5uu1h/nopm6M7Vi/osVzyLI9p712rYtFWQQI58oC4OqP1vD02DYs2nmSLUcy7X5vhaKq4tORhZTyuP7/NPAr2pzDKd28hP7f1CqlAo0sTm8IHHdSbnuvT6WUPaSUPWJiDFOb+IV/bMw2qRnnDevd991m0nMLKkKkCuci0RXOpmus+GXzMbYcyfSpLApFReMzZSGEiBBCRJq2gVHADmA+YPJomgjM07fnA7fqXlF9gCzdTLUYGCWEiNYntkfpZVWC0GDrr3jA68sd1u328lJfi+MXLoaRxabDGYx8e6Vbdc3OAArFRYQvRxZ1gUQhxDZgA7BASrkImAaMFELsB0bq+wB/AAeBZOAz4H4AKWU68DKwUf97SS+rEoQHB/pbBI/xdszARaAruPqjNf4Wwa+czcnn81UH7Z6N+IQFTJm/009SKSoSl3MWQoj+wBSgiV5fAFJK2czZeVLKg0Bng/KzwHCDcgk84OBaXwBfuJK1MhJWxZTFhyuSSc244NVr7j6RTcrZXEa0rUt8nQivXltRMTzx4zaW702jZ3wtOjeqaXXsqzUpTLm8vZ8kU1QU7kxwzwAeAzYBF4fDfAUSHOjZ4G32xiM89fN2dr44mojQissgn1dYTH5hCW8s2uv1a/+f7kE1dcFuNeFbRTHlAks/r82rpecWcMgm1cnJrDzO5OTTIS6qwuVT+B53WrIsKeVCKeVpKeVZ05/PJbtI+HlzqmF5m3qRhuVP/bwdwJxeo6K47pO1dH5pSYXes6qQcZE6HnjCZn3C/mU9J9b9322yM831m7aM/2/vvOOjqtL//37SSAJJCAFCCRB6VUCaVGkCgoorinXt8FNBBddFxNW1fFV0VRTr4iq2RUSwsCI2iiIqECAU6SXSO6EEkhByfn/cO5OZyZQ7kykhnPfrlRczt8w8zNy555ynfJ7LX/uFPbmnyRw/h4Ubg5dRFyy2HjzJ+r3H7UKQGutYmbouEJF/AZ8DBbaNSqkVIbPqPCAxzrt7qiDMOfquWVsajTuOmisLd4WWtnKhVTuNgeWl7zfRu3nNUsf5Yt+xfGqlxAdupBf6vfST/bFe5fqHlZVFF6Aj8Czwkvn3YiiNqgj8secYjR6Z43H/wZMFHveBkX5ZUWQyHBnxYekOhlsPniRz/Bzmb9gfAYt8c7YMEfryWHBZFo6e8i3NHm0qEa/xo8fJmbPF7DuWz69bD3Hxc/P4Zs3eshlqgU7P/Fim88fNXMVtU5cGyZryj8+VhVKqTzgMOVfYnXua7hPn88I1F5JzKI97+zShipvYwpDJv3h9nf3HvQ8W7y02Cvkev6JV4MZaJJw3tB/WlR4Q7v9kJQAT526gb4v0sNlihSGTF3lMUrihc30+WbrD7T4bRcWKuCirFRrli2U5R6iZVIkGac5JCX+bsQrXS2b/8Xz7YyvFi6489uVapi/byYOXNgOMlcvgEBepHjzh/TfoixlZ7l3MFRWfKwsRSReRd0Vkrvm8lYjcGXrTyic2wbhxM1fz5sKtvGJR/sKVuOgomqVX8XqM4w8wlOz0UCho484eDS29zqvXt7N0nGs72T/2GJ9pziHvdkSCP/YcZ/mf7rWtrKRFF5VzVWFPKKW49u3fuORfC7nydeeJj7s4XJdnS6Rh7nKzevTFdLPjYJF5bRQrhVKKH9ftJ3P8nDLf2D2x/M8jzFtvvEeLx+a6ncz44nxpj2zFDfU+RhFcHfP5JmBMqAwq77j+9k8H6CqKjRZmj+7h9ZitB0963R8soj3MfN+7rSPTRnTh0cEtfb5GzsQhlmtK8s+c5Y0FW3h/sXOnwFAU7207eJIXvt0QUO2IrxXXle3qeN0PsHR7SUnQT5sOMm+955vRH3uOMfLDLM6EuJNUcbHiZEGR12PGm4kWEJx41r5j+W77la/bc5x/mo20AF6fbzQG+3nTQRo+8o194Fm/NzSqvsPe+o07PzDeI/9MMSM+zPI7oWHBhgOlMsMqIlYC3NWVUjNE5BEApVSRiFQ8Z7pFXG9orreT4mLFxG+9SWAZxERHER8bzYTBLYgSoVNmNYa+sdjpmA37wlMJ7Gmw8OYS6tO8BrtzT7Npf8mAdmFGVY/HO/LS95vsbrY2dVO4tFU6P6zbz+UXBs/toJTi4yU7eG3eZg6cKODGLvXJSE0k/8xZYqOjPP6fbZwtVuQcdr4BVK8Sx//u68HAST9zPL+I5ulJLHyoNzmH88hITaD/y6UrvG+buoxrOmQwqHUt+43PU2D1wU9XsXH/CbYcOEnL2skB/s/dc+z0Gb5YsYtbu2Xy3Nz1vLNoOxueHlTKxaaUYs3uY3yaFZz+HEopZq3YzUOfrQKM//vOI6dIio9h3voDTPl5m1PFu2183uZy8/169R6S4mNoXz+w7gTHTp1h7IxsS8fmF/l3e/P1vbqyYOMBmqUnUbdqgl/vE2msDBZ5IpKGeV+0SXGE1KpyzMiPljs9V8rIOU9JiCU6Sli586i9e5wrHRukkmW6NGz3qpG9Gnt9vwMn8qmZFJrMEBuBrKKn3t6ZzftPcOmkn4mLMRaotVLiyZk4hMzxngP7AOv2llw+17z9m/1xRmqi/4Z44Jcth+x9NQCKTCn4Fo99y2VtavHWzR28nn/ppJ+cOhoCDGxdi9opCXx2dze+WbOXhLhoMqtX9lloOHP5LmYu9+3ftgXSfQ1kgTDhizXMWb2X1nVT+HzFbgCO558hPjaazPFzqFs1gWbpVViw8WBQ3/f7dfvtAwUY13PPFzxL3nhiRtYuZmTtCjiDafqyHcy3KI6588hpaqeE7kZ++9RlpCTEsuqfA+zb8gqKaPPEd7x1UwcGtakVsvcuC1bcUA9i6DY1FpHFwIfAfSG1qhxx7PQZ2j75PT9vOshr80r3zl648QAXPf0DjSd8w2WvLvLai3p4x3okxxvj81XtramsT12cE4jZlskrKGLqL9t9H2jy6/i+ZP2jP1DiX26Y5l9Vtqe04MVbD/Ht2uBkweS5uFkcYwdz1+7zeb7rQAElsZXmtZIYawZiHfl/vbyKGvjE5vYKJEDsjiN5hRwzs5dsrpWCM8X2wT2v4KxdNn937umgDxQA77lcW6cKIuOUKPJjRvTwrNU8OCOb3FOl3VF7ck+z9eBJ3liwpdQ+q71eoKTI0UbO4TyUglfd3GOKzhbzwrcb7N9lpLCSDbVCRC4BmmNIfWxUSp03FS0jPsji2Okz3PKe+xS5vcdKgtDr9x5n+L9/c3scGAPEtKU7yN6Zy8DW1mYPocxUUkrR+p/uNRlfvLaUUgsAdRyWzrbAnr8zYU+KrCt35HL3xyuCkv/u+rG5u1ms3HGU5rWSSIxz/hkc8JBYEOejGv+RwS2ZtmQHJ3zEA1w5VVjEtoN59ha7ga4sCorO8uJ3G7m1WybTl+7kdfOGljNxiD0N+3Begd31NHP5Tt5Y4L6ZU7BYst1Zxi1SopL+1C1tP5TH9kN5CMJLw51/B7bOke644vVffF67nmJntt9STJQwftZq5m84wNJHjUnZ3LX7eHPhVg6fLOT5ay50Ou+r7N0kxEYzwOL9pCxYyYa6GrgSY7BoBlwhIv3MHhUVkidm/8Fkc4RfmhM8zcLYaME2aXR30bi7SdRMDp0LyluXPldTtjxzGZufucxpW8vaydzUpT5v3HQRwWTGsp3szj3N16tLKdFbZreLvtXkeZvZ4tD7fEbWTv7y5q88ML20H7vzs/NKbQPfzY/AWk/1V3/c7FRD0+rx77j8tV/YecSwOVAhxy9X7uadRdvp8fwC+0ABcNcHy+wV2P/4cq1dCn/Fn+GXUS+r7tiN7/we0HkbAgiQz1qxi3c8uJQ98fu2wzzuELB3xXHSsvPIKfvEpMhh4jV92U4OOGR/2ZpouWahnS48ywPTsxn50XKueM17qn4wsBKzuBPoCtgcjb2B34FmIvKUUuqjENkWMd7/NQfAclc4X8y8uys1k+IREa7tUI+VO3Kp58Y/HyWlxbcWbjxAtcqxLN5ymLb1qjK8YwaVYoIjTugt6+Yil0BijJtZdXSU8MxfLii1vXNmtTINsuNmraZKpRhOFhQx5ILaSABumWe+We/0/Js1+/hmTYn7adzM1QBk77R+w7SyGkyIjSb/jPcBY9KPm4iNEe7t3cTt/kBn356+zh/Xl/jq88+ctU8SThSE30HgaYVulV+3BqY09H0AKbFgXEdN0qvQx2Il+vVTjMFsQKta3PzuEhaN60O9aiW/9SKHCZotdpMzcYjTysIVm/uzqFixJ/e0fXXv6LLypwAyUKzELIqBlkqpYUqpYUArDNmPLsDDoTSuotAxsxr104wL5obO9dj27GC3KwZ3HqdFmw8x9tNVzFy+i8e+XMsTs/+gsKiYsZ9ms/NI2eoSilxWFvf3a0rOxCFGkLoM6rDv3d6JCYNbAFCtclxAr2FL7SwMcRqpLX//wPF8juef8VrbYqVI7LO7u3GZhQBlfqFn330g/+XM8XOY8MUan8c5riZdv3+Ne26fusyuMmCVm99dAsD901c6bfe08rR9F1kONT0f/f4n+WfO2jMHwXmCl73Tuf4nc/wcMsfP4bAPdYhAsbKyyFRKOQ7LB4BmSqkjInLexC6ChUiJK8oVK8U9q3cd49eth/hi5W4O5xXy4R2d/Xr/l3/YxOR5m3mgX9NSwbSVO9wXn/lLlUoxjOzVmAGtapFWJY4Pf/uT2dl7AmoKdOasIhziu55cT46kJMT6PKZJzSq8dXMHRnyY5bXA68ipQrJ35tLKTYqsLZYSasKVml0RuNslC9IqhUXFHMkrJDUxlgUbD7j9zE8WFLmdpDz25VqnjD4bFz39A42qV3YaWBwZ+MoiexJKMLHyM1wkIl8Dn5nPhwE/m93vKlzvyMIwC/j5S2FRsb2ZUCBxUFssxl3WRbCxrU5G9WnCuj3HAxssioqhUrAtc8ZqBa4ti8gKST5GuI9/38HHv+8g1k0cZPznaxj/+Rp+f6SfR0G9tbuPkZIQ6+TiOF94/Ku1PDW0jaVjZ6/aE5QkEV9abp7IOZTnswNmGw9JJu4QhCN5hV5bMB8K0crCytU/CpgKtAPaY6TOjlJK5VnRjRKRaBFZaQ44iEhDEVkiIptF5FMRiTO3VzKfbzH3Zzq8xiPm9o0iMtD//6Z1ynsL0BP5RRw4YcxCHFMss3fm2tMjC4uKA+pgFqyUTXd4mym/fmN7j/v8SXm04anPuSd+3hz8lNGbuzawdJy3JIPn5q73uO/y136h5wsLOHb6jF/ukWDSq1npXvdWVl++uMTN6zry4W9/+nwN2wBx/ycrGfNp6SQGX4O5K7kBpq3meXE3BsLq3ZGbn3sdLEQkGvhBKTVLKTVWKTVGKTVT+Zeu8QDgeNU/D0xSSjUFjmIE0DH/PaqUagJMMo9DRFoB1wOtgUHAm6ZdISGQm1M42Xc8397zwrHC+Ko3FtuDa7YaA1ug3iqNa3jXqioLo/o0YdqILm73eatW7vTMj3zuoSeIIyfyz1BcrPh1yyGufH2xz+Md8ZTKa6Nn0+rc0LmeX6/ZPL3sbiQrQ/c/3LgpQk23xmn0bVGTf9/cgQZpziub2aO7e+zVYpVLW5VNTHLW8l00mvANT/7P/WSpS8NqrHmy9Jzz4zvdX5/lidHTVvo+KER4HSyUUmeBUyISUOsrEckAhgD/MZ8L0BeYaR7yAXCV+Xio+Rxzfz/z+KHAdKVUgVJqO0aPbv8c9X5wNoJBv0nXteXiRtXY8PQgZo/u7vP4bQfzWL0rl2fmGA1pNu4/YS+yCoRbu1mbDQdCdJTQrXF1pt7eqdS+2j56Fzw4Y5XHfcv/PMLEuRu44InvaTThG278zxKvS3R3TPbhkpt8fXueu/pCr8e4Eowuh1+v3svUxdvJHD+HqQ5BTsfirP+tCjy9OFDqpSby3m2dSIiLpm8L5yyhSjHRzLi7a5lePzWxJCnCVsTqD+8sMtJdPRW05puuZtfVRZdG1fx+r/MJK26ofGCNqTw72fZn8fVfAcZhZFQBpAG5Silb1dIuwFbKXBfYCYb+FIakSJrjdjfn2BGRkSKSJSJZBw8G7laIpEroX9pnMH1kV+Jjoy2nx175+mLeWVRyIxky+RcnV5o/YoShkJlwxZ3/2LUozh+GvfUbb/8UusKytU8OJDXAjK6v7/MuFOmLomLFk2ZnOlu+/9G8QrYfjoxoXY0kI3jkeH09Oril0/8zLiaK5PiyuaIcL8P3PSRwvOuH6oArDcw4z1eju/P3gc3t292lrWpKsDJYzAEeA37G6MNt+/OKiFwOHFBKOR7r7ttQPvZ5O6dkg1JTlFIdlVIda9Tw7vP0xsIgSR4Mal2LhNhobrXou3alLIF2RxG4/i//5OVIZ2Ki/OsXHgjefPTljam3dXLbq8Qq/vZf98a+4/l8vXoP7Z/+gave8M/NZoWr2tXhi3u7sWRCv1L70pONQcJWSLjPIXMnJjrKqed2VTNm8UC/pgHb4hg6c633sfH01+tKbZu/YT8jPszymeKcnGB8p41qVGFUn5JaFxFh67ODA7C4fHH1RdakhPzFitzHByKSANRXSm3047W7A1eKyGAgHkjGWGlUFZEYc/WQAdjW0buAesAuEYkBUoAjDtttOJ4TdLwFFf3h7b96F6rzhZVKYE+88G3J16QUTPphEykJsXy/zrsmUjgmVm3qGvGJhwe14HkL6rwRpYyfRxDHCopVaP3VV7StU0rR9b3bOjJ96U4yUhN5b/F2e+2LO7XUBmmJHD5ZSJR5EY29tBnXd65H1+c8y2N4on41/2p89uSepnZKPHe8b6i/9mpa3evx4y9zltwfN6g5b8w3Kt7DsboONf+6xr1UT1mxIvdxBZANfGs+bycis32dp5R6RCmVoZTKxAhQz1dK3YRRCX6NeditwFfm49nmc8z9881A+mzgejNbqiHQFAhZL8NDJ/3zdzvStp4h0e0rm8MKwSyYenXeZp76eh2/b/NeVR2OOX9GaiI5E4dwT+/GfHhHZ972of5q48uVuwN+z6Y1Awvcu6uB8I9z48Yzuk8Tp17ZtsLMvi3SmXJLRwa2NgLOvZoa13X9tNLpuvMevISVj1/qtK12SgJzH+jp8/1tr59WOY5F4/rQqo7z5/71fT3cDlA5h/LI3plLt4nz7c2TANb7qB9xXS3e27sJfzw1yKed5wI9mlQP2YBnZe7zBEZAORdAKZUNWGud5p6HgQdFZAtGTOJdc/u7QJq5/UFgvPl+fwAzgHUYA9YoM/AedGwpqe746e+9vZ6bM3EI9/c1lrTB+LJsS/9wEm6fba9mNSzLMbtLf7TK6zf6r12VM3EI6SHU5SpPPDSwuddrtkujNHImDrFPhuLdxNNioqPcut2s9OUY2asRNZMq8dTQNk51I7eYLtw2dVNoV790r5Rxs1YzbYmRRrvMQV5m2hLvrW6tcvcl3tsHrHliAM9dfQFvBlkbrSz0aRE6yT4rDtkipdQxF30evyahSqmFwELz8TbcZDMppfKBaz2c/wzwjD/vGQh7cj0PFq59iN1hi90G455r5f2Cxcy7u7Is5yhpVcI/QPnDkbxCPsvaychejRARy4J7zdKrMKZ/U175MfSFiI7YfjKNqlcu1cwnEtSrlmAXKwyEey5pTHGx4qaL6wfFntTEWJY+2p/Y6Ci7wqoNV/VWd10Yl24/4tSJ0ApWsquWTOjHgeMFXJCRwoZ9xz3GMWOjo7ihc30Ki4rp16ImxUqxYONBkirF+K08fC5gZWWxVkRuBKJFpKmIvAb8GmK7IoLVe/y0u5zzsW0XcscGqSTHx3CPB4G48kjHBql0zKzGPb29z6LKA0MmL+K5uRsY+2k2R/MKLVVeb3h6ECLCmP7NSAogDTOcBKOgzRtj+5fuweEPCXHRPDSwedCELHs3r2k5CSAxzvt72ho6+WL1E75retOT47kgwwjav3Kd577yNtvjYqJ497ZOTBxmpFeQRgORAAAReElEQVTfdHHoUtB9scBig6dAsPJN3YdREFcAfAIcp4L24La6XEp2+VE3NGUtUivHsfqJgXRoEFjrx3DTtl5VHr+iVaTNAKy53Wy9Q77M3sNDn62yVEDp2DbUahzofTe1IIHQMK0yt3RtwJRbSuIy2S5+fRtDLqzNvL9dEpT3dcQxNfSCuqXLpcLR2jNn4hC2PTvYqZf7rHu6MXFYacViT7i2fw0XVRM9p027uu7Sk41Oke3duMyC3SYXjLqs0X2cJ6YqhJFHn4OFUuqUUupRpVQnMzX1UdNldN5R2Zzd1DTzzW2VpqFSyRhiQeW0LHw1qrvlvtmh5rsxvfhqlO9CRBvzNhzgrYXe6ytcCwB9rUSiBMb0b+oU7C0LUVHCU0Pb0KRmSUVz1cQ4++TCkW6N04IWDv/PLR0B6JSZyqg+TezXqeOKwKYKfEePsoQfrRMVJYzo1YhKpr5W81pJfq1QQilF44sXrvGvINNdLVHz9MCSLBo5XCu2mJGNC+qmcE2HDKdtEsKkCo/rchH5H14m20qpK0NiUTmjUY3KXG22QP1ubC+2HsyjZnI8Wf/oz75j+fywbj+hkpN646aLmBMi3Z9F43zKeoWVqolxVE2MY+FDven94kJL5/gSQ3TtQeArHXnFY5d6nUkGC3fuphs71w9Yf6h+tUSmjehCj+eN/gg9mlanZ9PqPDrEmMm/dkN79uSeJj6uZG44sldjn/3fQ4Htpu/vLS3NLIzMSE0ocwMlf/lL+7rsyT3NNR0y2HH4FC1rJ9tThN3huuK4tWsD+rdK58vs0hn/r1zXjsnzNrPtUB4iON1LRvRsyIhejej8jKGIPLB1Oqsc+q/YJiFPD23NY18Z0iaBtgSwgreVxYvAS17+KjypibHM/1tvRvc1CowyUhPtabHVq1QKy2zH0We66vEBXo60zls3XVRu1Uozq1dm3KDmvg8MANdBvaOLu9Cq+mwg/PeuLnw7xkgjffvmDlRNjOXl4W25o3tDpo+8GBGhamIsf7u0GSP97OV9R/dMMlIT+fdfOzDtri7Ex0bz0Z1daFHLcH3Ex0bTqEYVe9FlJEsJejc3fj/+Zgze3j2Tl4e35ccHg++q80VsdBRj+jcjIzWRbk2qk1o5zmt8qV/LdKeixHrVEunZtIbblqtXta/LE1e2BqB74+p8O6YnPc06kUox0U73mHsuaczKxy4t5cn4a9dMe0xn/GUtAv5/+sLjykIpZb30t4LRNiOFr0b7lmrIqGb4e2/vnhkyW65qX5f05HiO5BWSkhjrtg8FwNB2dfjKzczFHWUVags19/Zuwr29mzB+1mqn/Plg0dOceQtC1p9HGdAqne/X7SfBRxC1LHRvUlIoVislnmxz4L/aIetSRLjPvMlM8aOdZ7QZaPXVyc92g/ZHaj3YTLquHQ8Pyvc7BhETHcXVFxkulwUP9aaPxdVnJIiOEsZe2oyM1AT+PnM1jX3U+djckn1b1KRFrWQuv7A2izYfomH1ylR3yFAUEVIrx/Hr+L6c9qBmG8okDm9uqDW4d0MJUKyUCk2ZYAQ5a7opvC0xHUmOj/XZoD0YdG2cZn/sLkgJRjHOvmP5LDFTCWOjhZpJ8ezOLb1kP1eqVCcOu5A/D5/it22BtdL0xCvXtSOtSiWUUgzvWI+k+Bjyi86WSaMqkliNbdm+9kj6/+Njo8vUhRGgVoD1L6GOAbpybcd6tKtXlSY+Bot61RJZ9fgAuwzJ8I71qJWS4LESvXZK6aSEuJgoThWeDelv29uv43I32wRDbmNCaMyJLAWmHlNcMHUagkz/Vun88nAf/vXdRr7K3sPIXo2YuXwXvZvXpHfzmnR65kfAEMDbm5vv1v8fSE/rSBGKPtG2FYSIkJJouBPK20Dxw9hebNx/wqfEx0MDmln2U9vcUGWVEI80/twQuzSsxqA2tejRpHpEXK9NLUrV265DMK5Lf1UgPh3ZlW/W7A3pdezNDWXvMCIi7YAbgeHAdmBWyCyKIK3rpJAYF80D/QMXQQsHGamJNKpuzFZ6Na3BBIeUxB5NqrN2zzEqxZR9BldRcVeBXN5omp7E6TMlroaciUPcNjm6zI/ZckJcNP+9qwut6wQ/jTOcxEYLmWmJ5Bz23eSqXf2q3N49PBlfVri6fV0+L4N0jSea10oKeSteb26oZhiaTjcAh4FPAbHSHe9cJSUhlnXniEbMqD6NaV+/Kj1clqofuxQMVoqJoqComB8f7MXwf//ud6+HSHM2iIrxbeoms3b3cctuxvLOuqcG+j2TdIydnKuICAv/3sdSh8Bg6LQFk5eGt+XFa9uS9edRqlW2VoT58vC2EY0z2fB2pW0AFgFXKKW2AIjI2LBYpfFJTHSU27aWrth6D6Qnx7Pgod7sPRbetMOyEkj/5Nmju5N/pvQoM23ExRw4fu6UCNnTTF3Gtiva1qHobHG5c52VJxrXqMx3Y3oRU85cyiKCCHRuaL3Rki2wH2m8XW3DMFYWC0TkW2A654qMpsaOrX9EYlwM0VESckmJYGO1GVVCbLTdbeOp0DA5PrbMjXnCiW2wsPV0sGk7PTSgWVi1w8orcx/oyWWvLnK773BeYbkbKM51PH6aSqkvlFLXAS0wRADHAuki8paIBCfhXxNyZt3TlTu6NzxnMqBceXRIS3vFvDda1jb8teVJAbSstKydxH19m/DaDe2BkurcUBWBnmu0rJ3MWode2i9d29ZebX1xwzRPp2kCxIrcR55S6r9KqcsxMqGyMeXDNeWfDg2qlRv9p0Do2yLdSZG0T3P3rrd61Yw+Gb66pJ1LiAh/G9CcOmHQbzpXcexNMaxDhj1GcVsIa5/OV/xyeiqljgD/Nv80mrAz9fbOlgKbFZEiM9ofE31urhJDxcvD25JqyrTYxPw0wUdHyDTnBP8b3YP8opD0vDpnqJkcz55j/lc/V3TKSwC4ohOyCJCIxIvIUhFZJSJ/iMiT5vaGIrJERDaLyKciEmdur2Q+32Luz3R4rUfM7RtFxLcgvabCcUFGCp0yjQySfwxpWWr/+eDHn3JLB169vp2TBIRGEy5CmS5QAPQ1ZUHaAYNE5GLgeWCSUqopcBS40zz+TuCoUqoJMMk8DhFphZGV1RoYBLwpInpqdR7jLi5xHowV1EyKZ2i7upE2Q3OeErLBQhmcNJ/Gmn8K6AvMNLd/AFxlPh5qPsfc308MXYqhwHSlVIFSajuwBTdtWTXnD3WqJjDlrx18H6jRaIJGSBORRSRaRLKBA8APwFYgVylla1C7C7BNleoCOwHM/ceANMftbs5xfK+RIpIlIlkHD7rvmaupOAxwUVj9S/s6EbJEozk/CGmAWyl1FmgnIlWBL4DSzuYSD4K7FA/lZbvre00BpgB07NjxfPBKnPe8PLwtDdIqnzNtbDWac5mwZEMppXJFZCFwMVBVRGLM1UMGYGvCsAuoB+wSkRggBTjisN2G4zma8xidBaPRhI9QZkPVMFcUiEgC0B9YDywArjEPuxX4ynw823yOuX++UkqZ2683s6UaAk2BpaGyW6PRaDSlERWinEMRuRAjYB2NMSjNUEo9JSKNMHSmqgErgZuVUgUiEg98BLTHWFFcr5TaZr7Wo8AdQBEwRik118d7HwT+9HaMD6oDh8pwfqjQdvmHtss/tF3+URHtaqCUciuTELLB4lxGRLKUUh0jbYcr2i7/0Hb5h7bLP843u7Qso0aj0Wh8ogcLjUaj0fhEDxbumRJpAzyg7fIPbZd/aLv847yyS8csNBqNRuMTvbLQaDQajU/0YKHRaDQan1T4wUJEBpnS5ltEpFSHv0Ck0X29ZiTsEpF6IrJARNabkvAPlAe7HPZFi8hKEfm6vNglIlVFZKaIbDA/t67lxK6x5ne4VkQ+MWuQwmKXiKSZ19FJEXnd5ZwOIrLGPGeyiPjdhSnYdolIoojMMb/DP0Rkor82hcIul3Nni8ja8mKXiMSJyBQR2WR+bsMsGaOUqrB/GAWBW4FGQBywCmjlcsy9wNvm4+uBT83HrczjKwENzdeJtvKaEbKrNnCReUwSsKk82OVw3oPANODr8vA9mvs+AO4yH8cBVSNtF4ZI5nYgwTxuBnBbGO2qDPQA7gZedzlnKdAVQ69tLnBZpO0CEoE+Dt/hovJgl8N5V5vX/dowX/fevscngf8zH0cB1a3YU9FXFp2BLUqpbUqpQozK8aEux/grjW7lNcNul1Jqr1JqBYBS6gSGtIq/zQ9C8XkhIhnAEOA/ftoTMrtEJBnoBbwLoJQqVErlRtou87gYIEEMjbRE/NdCC9gupVSeUuoXIN/xYBGpDSQrpX5Txl3mQ0raC0TMLqXUKaXUAvNxIbACQz8uonYBiEgVjEnS//lpT0jtwlDDeA5AKVWslLJU7V3RBwsr8ub+SqNbkkyPgF12zKVoe2BJObHrFWAcUOynPaG0qxFwEJgqhnvsPyJSOdJ2KaV2Ay8CO4C9wDGl1PdhtMvba+7y8ZqRsMuOGFp0VwDzyoldTwMvAaf8tCdkdpmfEcDTIrJCRD4TkXQrxlT0wcKKvLm/0uiWJNMjYJdxkjGbmYWhoXU80naJyOXAAaXUcj9tCaldGLP3i4C3lFLtgTzA3/hTKD6vVIzZYkOgDlBZRG4Oo11leU1fhMIu4yRjFfYJMFmZmnKRtEtE2gFNlFJf+GlLSO3CuO4zgMVKqYuA3zAmJz6p6IOFFXlz+zFiTRo9GJLpobALEYnFGCj+q5T63E+bQmVXd+BKEcnBWEb3FZGPy4Fdu4BdSinb6msmxuARabv6A9uVUgeVUmeAz4FuYbTL22s6unfCfd37YgqwWSn1ip82hcqurkAH87r/BWgmRpuGSNt1GGOlYxvEPsPqde9v0OVc+sMYRbdhzNJsAaLWLseMwjlANMN83BrnAOQ2jICTz9eMkF2C4Ud+pTx9Xi7n9iawAHdI7MIIhjY3Hz8B/CvSdgFdgD8wYhWC4Y++L1x2Oey/jdKB0WUYPWlsAe7B5cSu/8OYJEWF+7r3ZpfDvkwCC3CH6vOaDvR12P+ZJXsC+XDPpT9gMEZm0FbgUXPbU8CV5uN4jNF1C0a2RyOHcx81z9uIQ4aFu9eMtF0YmQ8KWA1km39+/ZhD9Xk57O9NAINFCL/HdkCW+Zl9CaSWE7ueBDYAazFk+yuF2a4cjNnpSYyZaytze0fTpq3A65gKEJG0C2O2rTASOmzX/V2RtsvltTMJYLAI4ffYAPgZ47qfB9S3YouW+9BoNBqNTyp6zEKj0Wg0QUAPFhqNRqPxiR4sNBqNRuMTPVhoNBqNxid6sNBoNBqNT/RgodF4wVTvzDb/9onIbofnv4boPduLiEcdLRGpISLfhuK9NRpPxETaAI2mPKOUOoxRj4GIPAGcVEpZkkcoAxPwIj6nlDooIntFpLtSanGIbdFoAL2y0GgCRkROmv/2FpGfRGSG2SNgoojcJCJLxej/0Ng8roaIzBKRZeZfdzevmQRcqJRaZT6/xGEls9LcD0YR4U1h+q9qNHqw0GiCRFvgAeAC4K9AM6VUZwxZ9vvMY14FJimlOgHDcC/ZbquStvEQMEop1Q7oCZw2t2eZzzWasKDdUBpNcFimlNoLICJbAZus+Bqgj/m4P9BKShrMJYtIkjL6j9iojSGdbmMx8LKI/Bf4XCllkwk/gKFKq9GEBT1YaDTBocDhcbHD82JKfmdRQFel1Gk8cxpD7wcApdREEZmDoRH0u4j0V0ptMI/x9joaTVDRbiiNJnx8D4y2PTF7HriyHmjicExjpdQapdTzGK6nFuauZji7qzSakKIHC40mfNwPdBSR1SKyDqM/shPmqiHFIZA9RkTWisgqjJXEXHN7H2BOOIzWaACtOqvRlDdEZCxwQinlrdbiZ2CoUupo+CzTnM/olYVGU/54C+cYiBMiUgN4WQ8UmnCiVxYajUaj8YleWWg0Go3GJ3qw0Gg0Go1P9GCh0Wg0Gp/owUKj0Wg0PtGDhUaj0Wh88v8BeemB606wN9AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "capture_index = 30\n",
    "fs = 256000\n",
    "time = [t/fs for t in range(mode2_x.shape[1])]\n",
    "fig, axes = plt.subplots(3, 1, sharey=True, sharex=True)\n",
    "axes[2].set_xlabel('Time (s)')\n",
    "for i, label in enumerate(labels):\n",
    "    axes[i].plot(time, modes_x[i][capture_index,:])\n",
    "    axes[i].set_ylabel(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency Domain Information\n",
    "\n",
    "With this frequency aspect in mind it can be more impactful to differentiate data in the frequency domain. In this case a common technique is to transform sequences or time domain data into image data in the form of spectrograms. This way we can treat the problem as an image classification problem which many ML algorithms are designed to handle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def spec_data(x, fs, plot=False, title=\"\"):\n",
    "    f, t, Sxx = signal.spectrogram(x, fs, nfft=x.shape[0])\n",
    "    Sxx = np.abs(Sxx)\n",
    "    if plot:\n",
    "        plt.pcolormesh(t, f, Sxx, shading=\"gouraud\")\n",
    "        plt.ylabel(\"Frequency [Hz]\")\n",
    "        plt.xlabel(\"Time [sec]\")\n",
    "        plt.title(title)\n",
    "        plt.show()\n",
    "    return Sxx\n",
    "\n",
    "spec_data(mode2_x[capture_index,:], fs, True, labels[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec_data(mode3_x[capture_index,:], fs, True, labels[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec_data(mode4_x[capture_index,:], fs, True, labels[2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a simple inspection we can clearly see fading a frequency information across labels, which the network needs to be trained to distinquish.\n",
    "\n",
    "Next this process will be applied to all data captures and split the data into training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply spectrogram\n",
    "for i in range(x.shape[0]):\n",
    "    x_seg = x[i, :]\n",
    "    sxx = spec_data(x_seg, fs) / x_seg.shape[0]\n",
    "    sxx = sxx.reshape((1, sxx.shape[0], sxx.shape[1]))\n",
    "    if i == 0:\n",
    "        x_sxx = sxx\n",
    "    else:\n",
    "        x_sxx = np.vstack((x_sxx, sxx))\n",
    "x = x_sxx\n",
    "\n",
    "# Shuffle labeled captures\n",
    "idx = np.random.permutation(len(y))\n",
    "x = x[idx]\n",
    "y = y[idx]\n",
    "\n",
    "# Split into train and test\n",
    "train_percent = 0.5\n",
    "columns = x.shape[0]\n",
    "train_columns = int(np.floor(train_percent * columns))\n",
    "\n",
    "x_train = x[:train_columns, :]\n",
    "y_train = y[:train_columns]\n",
    "x_test = x[train_columns:, :]\n",
    "y_test = y[train_columns:]\n",
    "\n",
    "num_classes = len(np.unique(y))\n",
    "y_train = utils.to_categorical(y_train, num_classes)\n",
    "y_test = utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "# Add a fourth column for input of tensorflow model\n",
    "x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], x_train.shape[2], 1))\n",
    "x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], x_test.shape[2], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN and NN Model Implementations\n",
    "\n",
    "With the training and testing data prepared a model can be constructed to correctly categorize or classify the spectrums into the different labels. Two models have been built for this purpose. One based on using simple neutral nets with just dense layers, and a second using convolutions layers which are very common in image processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model_nn(input_shape, n_outputs):\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(32, activation=\"relu\", input_shape=input_shape))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation=\"relu\"))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(num_classes, activation=\"softmax\"))\n",
    "    # Compile\n",
    "    model.compile(\n",
    "        loss=losses.categorical_crossentropy,\n",
    "        optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
    "        metrics=[\"accuracy\"],\n",
    "    )\n",
    "    return model\n",
    "\n",
    "def create_model_cnn(input_shape, n_outputs):\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(4, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n",
    "    model.add(Conv2D(8, kernel_size=(3, 3), activation=\"relu\"))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(32, activation=\"relu\"))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(num_classes, activation=\"softmax\"))\n",
    "    # Compile\n",
    "    model.compile(\n",
    "        loss=losses.categorical_crossentropy,\n",
    "        optimizer=keras.optimizers.Adam(learning_rate=0.0004),\n",
    "        metrics=[\"accuracy\"],\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# Construct models\n",
    "input_shape = (x_train.shape[1], x_train.shape[2], x_train.shape[3])\n",
    "model_nn  = create_model_nn(input_shape, num_classes)\n",
    "model_cnn = create_model_cnn(input_shape, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of these models do provide similar classification performance but the CNN is about 150x less parameterize to optimize and will converge much faster than the basic NN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 2049, 18, 32)      64        \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1180224)           0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               151068800 \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 387       \n",
      "=================================================================\n",
      "Total params: 151,069,251\n",
      "Trainable params: 151,069,251\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model_nn.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 2047, 16, 4)       40        \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 2045, 14, 8)       296       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 1022, 7, 8)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1022, 7, 8)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 57232)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                1831456   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 3)                 99        \n",
      "=================================================================\n",
      "Total params: 1,831,891\n",
      "Trainable params: 1,831,891\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model_cnn.summary()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "19/19 [==============================] - 2s 95ms/step - loss: 1.0953 - accuracy: 0.5067 - val_loss: 1.0911 - val_accuracy: 0.3667\n",
      "Epoch 2/50\n",
      "19/19 [==============================] - 2s 85ms/step - loss: 1.0956 - accuracy: 0.3533 - val_loss: 1.0848 - val_accuracy: 0.3467\n",
      "Epoch 3/50\n",
      "19/19 [==============================] - 2s 83ms/step - loss: 1.0782 - accuracy: 0.5000 - val_loss: 1.0655 - val_accuracy: 0.6400\n",
      "Epoch 4/50\n",
      "19/19 [==============================] - 2s 84ms/step - loss: 1.0648 - accuracy: 0.3667 - val_loss: 1.0488 - val_accuracy: 0.3267\n",
      "Epoch 5/50\n",
      "19/19 [==============================] - 2s 84ms/step - loss: 1.0286 - accuracy: 0.4533 - val_loss: 1.0004 - val_accuracy: 0.6600\n",
      "Epoch 6/50\n",
      "19/19 [==============================] - 2s 86ms/step - loss: 0.9690 - accuracy: 0.5600 - val_loss: 0.9345 - val_accuracy: 0.5667\n",
      "Epoch 7/50\n",
      "19/19 [==============================] - 2s 84ms/step - loss: 0.8945 - accuracy: 0.6200 - val_loss: 0.8560 - val_accuracy: 0.6200\n",
      "Epoch 8/50\n",
      "19/19 [==============================] - 2s 84ms/step - loss: 0.8091 - accuracy: 0.6733 - val_loss: 0.7951 - val_accuracy: 0.6667\n",
      "Epoch 9/50\n",
      "19/19 [==============================] - 2s 83ms/step - loss: 0.7437 - accuracy: 0.6600 - val_loss: 0.7255 - val_accuracy: 0.9867\n",
      "Epoch 10/50\n",
      "19/19 [==============================] - 2s 86ms/step - loss: 0.6532 - accuracy: 0.7800 - val_loss: 0.6468 - val_accuracy: 0.6400\n",
      "Epoch 11/50\n",
      "19/19 [==============================] - 2s 86ms/step - loss: 0.6161 - accuracy: 0.7200 - val_loss: 0.6152 - val_accuracy: 0.7200\n",
      "Epoch 12/50\n",
      "19/19 [==============================] - 2s 85ms/step - loss: 0.5793 - accuracy: 0.7333 - val_loss: 0.5712 - val_accuracy: 0.6600\n",
      "Epoch 13/50\n",
      "19/19 [==============================] - 2s 87ms/step - loss: 0.5540 - accuracy: 0.6733 - val_loss: 0.5336 - val_accuracy: 0.7267\n",
      "Epoch 14/50\n",
      "19/19 [==============================] - 2s 91ms/step - loss: 0.5252 - accuracy: 0.7400 - val_loss: 0.5047 - val_accuracy: 0.7667\n",
      "Epoch 15/50\n",
      "19/19 [==============================] - 2s 98ms/step - loss: 0.4774 - accuracy: 0.8333 - val_loss: 0.4801 - val_accuracy: 0.8600\n",
      "Epoch 16/50\n",
      "19/19 [==============================] - 2s 89ms/step - loss: 0.4560 - accuracy: 0.9200 - val_loss: 0.4566 - val_accuracy: 0.9333\n",
      "Epoch 17/50\n",
      "19/19 [==============================] - 2s 90ms/step - loss: 0.4423 - accuracy: 0.8933 - val_loss: 0.4419 - val_accuracy: 0.9400\n",
      "Epoch 18/50\n",
      "19/19 [==============================] - 2s 115ms/step - loss: 0.4202 - accuracy: 0.9200 - val_loss: 0.4124 - val_accuracy: 0.9667\n",
      "Epoch 19/50\n",
      "19/19 [==============================] - 1s 77ms/step - loss: 0.4042 - accuracy: 0.8400 - val_loss: 0.4126 - val_accuracy: 1.0000\n",
      "Epoch 20/50\n",
      "19/19 [==============================] - 2s 82ms/step - loss: 0.3835 - accuracy: 0.9267 - val_loss: 0.3947 - val_accuracy: 0.9600\n",
      "Epoch 21/50\n",
      "19/19 [==============================] - 2s 87ms/step - loss: 0.3796 - accuracy: 0.9067 - val_loss: 0.3615 - val_accuracy: 0.9667\n",
      "Epoch 22/50\n",
      "19/19 [==============================] - 1s 79ms/step - loss: 0.3442 - accuracy: 0.9600 - val_loss: 0.3398 - val_accuracy: 0.9600\n",
      "Epoch 23/50\n",
      "19/19 [==============================] - 2s 87ms/step - loss: 0.3259 - accuracy: 0.9467 - val_loss: 0.3244 - val_accuracy: 1.0000\n",
      "Epoch 24/50\n",
      "19/19 [==============================] - 2s 85ms/step - loss: 0.3157 - accuracy: 0.9667 - val_loss: 0.2991 - val_accuracy: 0.9867\n",
      "Epoch 25/50\n",
      "19/19 [==============================] - 2s 120ms/step - loss: 0.2880 - accuracy: 0.9467 - val_loss: 0.3120 - val_accuracy: 0.9467\n",
      "Epoch 26/50\n",
      "19/19 [==============================] - 2s 94ms/step - loss: 0.2708 - accuracy: 0.9600 - val_loss: 0.2599 - val_accuracy: 0.9667\n",
      "Epoch 27/50\n",
      "19/19 [==============================] - 3s 136ms/step - loss: 0.2384 - accuracy: 0.9733 - val_loss: 0.2444 - val_accuracy: 0.9733\n",
      "Epoch 28/50\n",
      "19/19 [==============================] - 2s 128ms/step - loss: 0.2431 - accuracy: 0.9533 - val_loss: 0.2232 - val_accuracy: 0.9733\n",
      "Epoch 29/50\n",
      "19/19 [==============================] - 2s 87ms/step - loss: 0.2507 - accuracy: 0.9467 - val_loss: 0.2024 - val_accuracy: 0.9867\n",
      "Epoch 30/50\n",
      "19/19 [==============================] - 2s 97ms/step - loss: 0.1859 - accuracy: 0.9867 - val_loss: 0.1835 - val_accuracy: 0.9867\n",
      "Epoch 31/50\n",
      "19/19 [==============================] - 2s 92ms/step - loss: 0.1720 - accuracy: 0.9800 - val_loss: 0.1750 - val_accuracy: 0.9800\n",
      "Epoch 32/50\n",
      "19/19 [==============================] - 2s 91ms/step - loss: 0.1559 - accuracy: 0.9733 - val_loss: 0.1583 - val_accuracy: 1.0000\n",
      "Epoch 33/50\n",
      "19/19 [==============================] - 2s 92ms/step - loss: 0.1658 - accuracy: 0.9667 - val_loss: 0.1411 - val_accuracy: 1.0000\n",
      "Epoch 34/50\n",
      "19/19 [==============================] - 2s 89ms/step - loss: 0.1349 - accuracy: 0.9867 - val_loss: 0.1311 - val_accuracy: 1.0000\n",
      "Epoch 35/50\n",
      "19/19 [==============================] - 2s 83ms/step - loss: 0.1470 - accuracy: 0.9800 - val_loss: 0.1267 - val_accuracy: 1.0000\n",
      "Epoch 36/50\n",
      "19/19 [==============================] - 2s 90ms/step - loss: 0.1227 - accuracy: 0.9933 - val_loss: 0.1155 - val_accuracy: 1.0000\n",
      "Epoch 37/50\n",
      "19/19 [==============================] - 2s 89ms/step - loss: 0.1086 - accuracy: 0.9733 - val_loss: 0.1094 - val_accuracy: 0.9800\n",
      "Epoch 38/50\n",
      "19/19 [==============================] - 2s 94ms/step - loss: 0.1152 - accuracy: 0.9800 - val_loss: 0.0933 - val_accuracy: 1.0000\n",
      "Epoch 39/50\n",
      "19/19 [==============================] - 2s 101ms/step - loss: 0.1400 - accuracy: 0.9667 - val_loss: 0.1032 - val_accuracy: 0.9867\n",
      "Epoch 40/50\n",
      "19/19 [==============================] - 2s 95ms/step - loss: 0.1077 - accuracy: 0.9800 - val_loss: 0.0846 - val_accuracy: 1.0000\n",
      "Epoch 41/50\n",
      "19/19 [==============================] - 2s 102ms/step - loss: 0.1250 - accuracy: 0.9667 - val_loss: 0.0851 - val_accuracy: 0.9933\n",
      "Epoch 42/50\n",
      "19/19 [==============================] - 2s 99ms/step - loss: 0.1120 - accuracy: 0.9600 - val_loss: 0.0689 - val_accuracy: 1.0000\n",
      "Epoch 43/50\n",
      "19/19 [==============================] - 2s 94ms/step - loss: 0.0868 - accuracy: 0.9867 - val_loss: 0.0663 - val_accuracy: 0.9933\n",
      "Epoch 44/50\n",
      "19/19 [==============================] - 2s 108ms/step - loss: 0.0860 - accuracy: 0.9800 - val_loss: 0.0751 - val_accuracy: 1.0000\n",
      "Epoch 45/50\n",
      "19/19 [==============================] - 2s 96ms/step - loss: 0.0687 - accuracy: 0.9933 - val_loss: 0.0669 - val_accuracy: 0.9867\n",
      "Epoch 46/50\n",
      "19/19 [==============================] - 2s 96ms/step - loss: 0.0647 - accuracy: 0.9867 - val_loss: 0.0651 - val_accuracy: 1.0000\n",
      "Epoch 47/50\n",
      "19/19 [==============================] - 2s 101ms/step - loss: 0.0649 - accuracy: 0.9933 - val_loss: 0.0502 - val_accuracy: 1.0000\n",
      "Epoch 48/50\n",
      "19/19 [==============================] - 2s 95ms/step - loss: 0.0611 - accuracy: 0.9867 - val_loss: 0.0482 - val_accuracy: 0.9933\n",
      "Epoch 49/50\n",
      "19/19 [==============================] - 2s 95ms/step - loss: 0.0472 - accuracy: 1.0000 - val_loss: 0.0450 - val_accuracy: 1.0000\n",
      "Epoch 50/50\n",
      "19/19 [==============================] - 2s 95ms/step - loss: 0.0507 - accuracy: 0.9933 - val_loss: 0.0480 - val_accuracy: 0.9867\n"
     ]
    }
   ],
   "source": [
    "chosen_model = 'cnn'\n",
    "\n",
    "if chosen_model == 'cnn':\n",
    "    model = model_cnn\n",
    "    epochs = 50\n",
    "else:\n",
    "    model = model_nn\n",
    "    epochs = 100\n",
    "\n",
    "history = model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    batch_size=8,\n",
    "    epochs=epochs,\n",
    "    verbose=1,\n",
    "    validation_data=(x_test, y_test),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Studying the results of our model fit and validation, classification of the data sets easily exceeds 95% accuracy with a relatively small number of epochs. This is very attractive for such a simple model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
